419 research outputs found

    Untersuchungen zur Rolle der Ganzhirn CT-Perfusion in der akuten Schlaganfalldiagnostik

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    Untersuchungen zur Rolle der Ganzhirn CT-Perfusion in der akuten Schlaganfalldiagnostik

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    Magnetic Resonance Imaging of Gliomas

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    Open Access.This work was supported in part by grants CTQ2010-20960-C02-02 to P.L.L. and grant SAF2008-01327 to S.C. A.M.M. held an Erasmus Fellowship from Coimbra University and E.C.C. a predoctoral CSIC contract.Peer Reviewe

    Arterial Spin Labeling Perfusion of the Brain: Emerging Clinical Applications

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    Arterial spin labeling (ASL) is a magnetic resonance (MR) imaging technique used to assess cerebral blood flow noninvasively by magnetically labeling inflowing blood. In this article, the main labeling techniques, notably pulsed and pseudocontinuous ASL, as well as emerging clinical applications will be reviewed. In dementia, the pattern of hypoperfusion on ASL images closely matches the established patterns of hypometabolism on fluorine 18 fluorodeoxyglucose (FDG) positron emission tomography (PET) images due to the close coupling of perfusion and metabolism in the brain. This suggests that ASL might be considered as an alternative for FDG, reserving PET to be used for the molecular disease-specific amyloid and tau tracers. In stroke, ASL can be used to assess perfusion alterations both in the acute and the chronic phase. In arteriovenous malformations and dural arteriovenous fistulas, ASL is very sensitive to detect even small degrees of shunting. In epilepsy, ASL can be used to assess the epileptogenic focus, both in peri- and interictal period. In neoplasms, ASL is of particular interest in cases in which gadolinium-based perfusion is contraindicated (eg, allergy, renal impairment) and holds promise in differentiating tumor progression from benign causes of enhancement. Finally, various neurologic and psychiatric diseases including mild traumatic brain injury or posttraumatic stress disorder display alterations on ASL images in the absence of visualized structural changes. In the final part, current limitations and future developments of ASL techniques to improve clinical applicability, such as multiple inversion time ASL sequences to assess alterations of transit time, reproducibility and quantification of cerebral blood flow, and to measure cerebrovascular reserve, will be reviewed

    ์ƒ์ดํ•œ ์ƒ์šฉ ์†Œํ”„ํŠธ์›จ์–ด๋ฅผ ์‚ฌ์šฉํ•œ CT ๊ด€๋ฅ˜ ๋งต์—์„œ์˜ ๊ฒฝ์ƒ‰ ์šฉ์  ์ธก์ •: ๊ธ‰์„ฑ ๋‡Œ์กธ์ค‘ ํ™˜์ž์—์„œ ๋™์ผํ•œ ์†Œ์Šค ๋ฐ์ดํ„ฐ๋ฅผ ์‚ฌ์šฉํ•œ ์ •๋Ÿ‰์  ๋ถ„์„

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์˜๊ณผ๋Œ€ํ•™ ์˜ํ•™๊ณผ, 2021.8. ์†์ฒ ํ˜ธ .์—ฐ๊ตฌ ๋ชฉ์ : CT ๊ด€๋ฅ˜ ์˜์ƒ (CT Perfusion map, CTP) ๊ธ‰์„ฑ ํ—ˆํ˜ˆ์„ฑ ๋‡Œ์กธ์ค‘ ํ™˜์ž์˜ ์น˜๋ฃŒ ์—ฌ๋ถ€์˜ ์„ ํƒ ๊ฒฐ์ •๊ณผ์ •์— ์‹ค์ œ๋กœ ๋„๋ฆฌ ์‚ฌ์šฉ๋˜๊ณ  ์žˆ์ง€๋งŒ, ์ •ํ™•ํ•œ ๊ฒฝ์ƒ‰ ์ค‘์‹ฌ๋ถ€ ์šฉ์ ์„ ์˜ˆ์ธกํ•˜๋Š” ๋ฐ ์‚ฌ์šฉ๋˜๋Š” ์ตœ์ ์˜ ์ž„๊ณ„๊ฐ’๊ณผ ๋งค๊ฐœ ๋ณ€์ˆ˜์— ๋Œ€ํ•ด์„œ๋Š” ๋ช…ํ™•ํ•œ ํ‘œ์ค€์ด ์—†๋‹ค. ํ˜„์žฌ rCBF <30 %์˜ ์ž„๊ณ„๊ฐ’์„ ๊ฐ€์ง„ ๊ฒฝ์ƒ‰ ์ค‘์‹ฌ๋ถ€(Infarct core) ์šฉ์ ์ด ์ผ๋ฐ˜์ ์œผ๋กœ ์‚ฌ์šฉ๋˜๊ณ  ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ Follow-up diffusion-weighted imaging (DWI)์™€ ๊ฒฝ์ƒ‰ ์ค‘์‹ฌ๋ถ€(Infarct core) ์šฉ์ ์˜ ์ผ์น˜๋ฅผ ํ‰๊ฐ€ํ•˜๊ธฐ ์œ„ํ•ด CTP์™€ DWI ์‚ฌ์ด์˜ ์‹œ๊ฐ„๊ฐ„๊ฒฉ์ด 24 ์‹œ๊ฐ„ ์ด๋‚ด์ธ ์—ฌ๋Ÿฌ ์—ฐ๊ตฌ๊ฐ€ ์ง„ํ–‰๋˜์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ RAPID, singular value decomposition+ (SVD+) VITREA, BAYESIAN VITREA ๋“ฑ์˜ CTP ์†Œํ”„ํŠธ์›จ์–ด ํ”„๋กœ๊ทธ๋žจ์—์„œ ๋‹ค์–‘ํ•œ Deconvolution ๋ฐฉ๋ฒ•, ๋งค๊ฐœ ๋ณ€์ˆ˜, ์ž„๊ณ„๊ฐ’์— ๋”ฐ๋ผ ์ธก์ •๋œ ๊ฒฝ์ƒ‰ ์ค‘์‹ฌ๋ถ€ ์šฉ์ ๊ณผ ์งง์€ ์‹œ๊ฐ„ ๊ฐ„๊ฒฉ์œผ๋กœ (60๋ถ„ ์ด๋‚ด) ์‹œํ–‰๋œ DWI์—์„œ ์ธก์ •๋œ ๊ฒฝ์ƒ‰ ์ค‘์‹ฌ๋ถ€์šฉ์ ๊ณผ์˜ ์ผ์น˜์œจ์„ ํ‰๊ฐ€ํ•œ๋‹ค. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ•: ์ „๋ฐฉ ์ˆœํ™˜์— ์žˆ์–ด์„œ ํฐ ํ˜ˆ๊ด€์˜ ํ์ƒ‰์ฆ์„ ๊ฐ€์ง„ 42๋ช…์˜ ๊ธ‰์„ฑ ํ—ˆํ˜ˆ์„ฑ ๋‡Œ์กธ์ค‘ ํ™˜์ž๊ฐ€ ํฌํ•จ๋˜์—ˆ๋‹ค. CT ๊ด€๋ฅ˜ ์˜์ƒ์€ VITREA ๋ฐ RAPID์˜ SVD +์™€ Bayesian ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ํฌํ•จํ•œ ๋‹ค์–‘ํ•œ CT ๊ด€๋ฅ˜ ์†Œํ”„ํŠธ์›จ์–ด๋กœ ์ฒ˜๋ฆฌ๋˜์—ˆ๋‹ค. RAPID๋Š” ๊ฒฝ์ƒ‰ ์ค‘์‹ฌ๋ถ€๋ฅผ rCBF <20 % -38 %, rCBV <34 % -42 %์„ ๊ฐ€์ง„ ์กฐ์ง์œผ๋กœ ์‹๋ณ„ํ•˜์˜€๋‹ค. SVD+ VITREA์—์„œ๋Š” ๊ฒฝ์ƒ‰ ์ค‘์‹ฌ๋ถ€๋ฅผ CBV์˜ 26-56 % ๊ฐ์†Œ๋กœ ์ •์˜ํ•˜์˜€๋‹ค. BAYESIAN VITREA์—์„œ๋Š” ๊ฒฝ์ƒ‰ ์ค‘์‹ฌ๋ถ€๋ฅผ CBV์˜ 28-48% ๊ฐ์†Œ๋กœ ์ •์˜ํ•˜์˜€๋‹ค. Olea Sphere๋Š” DWI ๊ฒฝ์ƒ‰ ์ค‘์‹ฌ๋ถ€ ์šฉ์ ์„ ์ธก์ •ํ•˜๋Š” ๋ฐ ์‚ฌ์šฉ๋˜์—ˆ๋‹ค. CTP ์ค‘์‹ฌ๋ถ€ ์šฉ์ ์˜ ์ธก์ •๊ฐ’์€ DWI์—์„œ ๊ฒฐ์ •๋œ ์ตœ์ข… ๊ฒฝ์ƒ‰ ์šฉ์ ๊ณผ ๋น„๊ต๋˜์—ˆ๋‹ค. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ: CTP๋Š” ๋ชจ๋“  ํ™˜์ž์—์„œ DWI ์ „์— ์‹ค์‹œ๋˜์—ˆ๊ณ , CTP์™€ DWI ์‚ฌ์ด์˜ ์‹œ๊ฐ„์˜ ์ค‘์•™๊ฐ’์€ 37.5 ๋ถ„(min)์ด์—ˆ๋‹ค interquartile range (IQR) 20 -44. 42 ๋ช…์˜ ํ™˜์ž์—์„œ๋Š” ์ตœ์ข… ๊ฒฝ์ƒ‰ ์ค‘์‹ฌ๋ถ€ ์šฉ์ ์˜ ์ค‘์•™๊ฐ’์€ DWI์—์„œ 19.50 ml (IQR 6.91 - 69.72) ์˜€๋‹ค. RAPID rCBF <30% ๊ธฐ๋ณธ ์„ค์ •๊ฐ’์—์„œ ๊ฒฝ์ƒ‰ ์ค‘์‹ฌ๋ถ€ ์šฉ์  ์ฐจ์ด์˜ ์ค‘์•™๊ฐ’์€ (IQR) 8.19 ml (3.95 โ€“ 30.70), spearmanโ€™s correlation coefficient (r) = 0.759๋ฅผ ์–ป์„ ์ˆ˜ ์žˆ์—ˆ์œผ๋ฉฐ; SVD+ VITREA CBV์˜ 41% ๊ฐ์†Œ ์‹œ ๊ฒฝ์ƒ‰ ์ค‘์‹ฌ๋ถ€ ์šฉ์  ์ฐจ์ด์˜ ์ค‘์•™๊ฐ’์€ (IQR) 3.82 ml (-2.91 โ€“ 20.95), r = 0.717๋กœ, BAYESIAN VITREA CBV์˜ 38% ๊ฐ์†Œ ์‹œ ๊ฒฝ์ƒ‰ ์ค‘์‹ฌ๋ถ€ ์šฉ์  ์ฐจ์ด์˜ ์ค‘์•™๊ฐ’์€ (IQR) 8.16 ml (1.58 โ€“ 25.46), r = 0.754์ด์—ˆ๋‹ค. ๋ฐ˜๋ฉด ๊ฐ ์†Œํ”„ํŠธ์›จ์–ด์— ๋Œ€ํ•œ ์ตœ์ ์˜ ์ž„๊ณ„๊ฐ’์€ ๊ฒฝ์ƒ‰ ์ค‘์‹ฌ๋ถ€ ์šฉ์ ์„ ๊ธฐ๋ณธ ์„ค์ •๋ณด๋‹ค ์ •ํ™•ํ•˜๊ฒŒ ์ถ”์ •ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ์ž…์ฆ๋˜์—ˆ๋‹ค. ๊ฐ ์†Œํ”„ํŠธ์›จ์–ด์˜ ๊ฐ€์žฅ ์ •ํ™•ํ•˜๊ณ  ์ตœ์ ์˜ ๊ฒฝ์ƒ‰ ์ค‘์‹ฌ๋ถ€ ์šฉ์  ์ฐจ์ด์˜ ์ž„๊ณ„๊ฐ’์€ ๋‹ค์Œ๊ณผ ๊ฐ™์•˜๋‹ค: RAPID rCBF <38 % ๊ฒฝ์ƒ‰ ์ค‘์‹ฌ๋ถ€ ์šฉ์  ์ฐจ์ด๋Š” 4.87 ml (0.84 โ€“ 23.51), r = 0.752; SVD + VITREA CBV์ด 26 % ๊ฐ์†Œ ์‹œ ๊ฒฝ์ƒ‰ ์ค‘์‹ฌ๋ถ€ ์šฉ์ ์˜ ์šฉ์  ์ฐจ์ด๊ฐ€ -1.05 ml (-12.26 โ€“ 14.58), r = 0.679๋กœ ๋‚˜ํƒ€๋‚ฌ์œผ๋ฉฐ; BAYESIAN VITREA CBV์˜ 28 % ๊ฐ์†Œ๋Š” ๊ฒฝ์ƒ‰ ์ค‘์‹ฌ๋ถ€ ์šฉ์  ์ฐจ์ด๊ฐ€ 5.23 ml (-2.90 โ€“ 22.91), r = 0.685์˜€๋‹ค. ๊ฒฐ๋ก : ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” CBV ์ž„๊ณ„๊ฐ’์€ CBF ์ž„๊ณ„๊ฐ’๊ณผ ๋น„๊ตํ•˜์—ฌ ๊ธ‰์„ฑ ํ—ˆํ˜ˆ์„ฑ ๋‡Œ์กธ์ค‘ ํ™˜์ž์˜ ๊ฒฝ์ƒ‰ ์ค‘์‹ฌ๋ถ€ ์šฉ์ ์„ ์˜ˆ์ธกํ•˜๋Š” ๋” ์ •ํ™•ํ•œ ๋งค๊ฐœ ๋ณ€์ˆ˜๋ฅผ ์ œ๊ณตํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค.Purpose: Although using Computed Tomography Perfusion (CTP) for selecting and guiding decision-making processes of a patient with acute ischemic stroke has its advantages, there is no clear standardization of the optimal threshold and parameters used to predict infarct core volume accurately. Nowadays, infarct core volume with a rCBF<30% threshold is commonly used. However, several studies have been performed to assess the volumetric agreement of CTP infarct core volume with follow-up Diffusion-Weighted Imaging (DWI); the time between CTP and DWI was within 24 hours. In this study, we aimed to assess the volumetric agreement of estimated infarct core volume with different deconvolution methods, parameters, and thresholds on CTP software programs, including: RAPID, singular value decomposition plus (SVD+) VITREA, BAYESIAN VITREA, and also the final infarct volume on DWI with an especially short interval time (within 60 min) between CTP and follow-up DWI. Materials and methods: Forty-two acute ischemic stroke patients with occlusion of a large artery in the anterior circulation were included in the study. The CT perfusion maps were processed with different CT perfusion software, including SVD+ and Bayesian algorithms in VITREA and RAPID. The RAPID identified infarct core as tissue rCBF < 20-38% and rCBV < 34-42%. The SVD+ VITREA defined infarct core as CBV reduction of 26% - 56%. The Bayesian VITREA quantified infarct core as tissue CBV reduction of 28% - 48%. Olea Sphere was used to measure the infarct core volume on DWI. The CTP infarct core volume measurements were compared with the final infarct volume, which was determined on DWI. Results: The CTP was performed before DWI in all patients, and the median time between CTP and DWI was 37.5 minutes, with an interquartile range (IQR) of 20 โ€“ 44. In 42 patients, the median final infarct volume was 19.50 ml (IQR 6.91 โ€“ 69.72) with DWI. The most commonly used thresholds for each kind of CTP software, including RAPID rCBF<30%, resulted in a median infarct volume difference (IQR) of 8.19 ml (3.95 โ€“ 30.70), spearmanโ€™s correlation coefficient (r) = 0.759; SVD+ VITREA CBV reduction of 41% demonstrated a median infarct volume difference (IQR) of 3.82 ml (-2.91 โ€“ 20.95), r = 0.717; and BAYESIAN VITREA CBV reduction of 38% resulted in a median infarct volume difference (IQR) of 8.16 ml (1.58 โ€“ 25.46), r = 0.754. On the other hand, the optimal thresholds for each kind of software ended up estimating infarct core volume more accurately than the commonly used thresholds with lower infarct core volume differences. The most accurate and optimal infarct core volume thresholds for each kind of software were as follows: median infarct core volume difference (IQR) for RAPID rCBF<38% was 4.87 ml (0.84 โ€“ 23.51), r = 0.752; SVD+ VITREA CBV reduction of 26% was -1.05 ml (-12.26 โ€“ 14.58), r = 0.679; BAYESIAN VITREA CBV reduction of 28% was 5.23 ml (-2.90 โ€“ 22.91), r = 0.685. Conclusions: Our study found that the CBV thresholds provide a more accurate parameter to predict infarct core volume in acute ischemic stroke patients compared with the CBF thresholds.Chapter 1. Introduction 1 Chapter 2. Materials and methods 13 Chapter 3. Results 18 Chapter 4. Discussions 57 Chapter 5. Conclusions 64 Bibliography 65 Abstract in Korean 71์„

    Regional Low Cerebral Blood Flow Predicts Leukoaraiosis Development at 18 Months in Patients with TIA and Minor Stroke

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    The purpose of this study was to investigate whether low cerebral blood flow (CBF) is associated with subsequent white matter hyperintensity (WMH) development in minor stroke and transient ischemic attack (TIA) patients. New WMH at 18 months were identified by comparing follow-up with baseline FLAIR, and regions of interest (ROI) were placed in normal appearing and hyperintense white matter. Co-registered CBF maps were used to quantify relative CBF. Forty patients were evaluated, where mean age was 62+/-12 years, 78% male and 9% diabetic. A mixed effects logistic regression accounting for โ€œwithin patientโ€ clustering, showed that as CBF increases by 1mL/100g/min, the odds of having a new WMH decrease by 0.61. Results suggest that regions of white matter that develop WMH at 18 months have low baseline CBF. Future studies aiming to improve cerebral perfusion in normal appearing white matter might provide a target for arresting the development of WMH

    Cerebral blood perfusion changes in multiple sclerosis

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    The proximity of immune cell aggregations to the vasculature is a hallmark of multiple sclerosis. Furthermore, it is widely accepted that inflammation is able to modulate the microcirculation. Until recently, the detection of cerebral blood perfusion changes was technically challenging, and perfusion studies in multiple sclerosis patients yielded contradictory results. However, new developments in fast magnetic resonance imaging have enabled us to image the cerebral hemodynamics based on the dynamic tracking of a bolus of paramagnetic contrast agents (dynamic susceptibility contrast). This review discusses the technical principles, possible pitfalls, and potential for absolute quantification of cerebral blood volume and flow in a clinical setting. It also outlines recent findings on inflammation associated perfusion changes, which are inseparable from pathological considerations in multiple sclerosis

    Applications of CT Perfusion-Based Triaging and Prognostication in Acute Ischemic Stroke

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    CT Perfusion (CTP) is a minimally invasive imaging technique that aids acute ischemic stroke (AIS) triage and prognostication by determining tissue viability based on hemodynamic parameters. The goals of this research are to determine: 1) CTP thresholds for estimation of infarct and penumbra volume, 2) how CTP scan duration impacts infarct and penumbra volume estimates, and 3) reliability of CTP for predicting functional outcomes following intra-arterial therapy (IAT). Chapter 2 introduced an experimental study for determining ischemia-time dependent thresholds for brain infarction using multimodal imaging in a porcine stroke model that is easier to implement than previous large animal stroke models. CTP determined an absolute cerebral blood flow (CBF) threshold of 12.6ยฑ2.8mLโˆ™min-1โˆ™100g-1 for brain infarction after 3h of ischemia, which was close to that derived using hydrogen clearance in a previous study by Jones et al (Journal of Neurosurgery, 1981;54(6):773-782). Chapter 3 retrospectively investigated the impact of CTP scan duration on cerebral blood volume (CBV), CBF, and time-to-maximum (Tmax) and found optimal scan durations that minimized radiation dose while not under- or over-estimating infarct volumes measured using two previously derived CBF thresholds for infarction. We found that CBV and Tmax decreased at shorter scan durations, whereas CBF was independent of scan duration, consequently, infarct volume estimated by both CBF thresholds was independent of scan duration. Chapter 4 compared reperfusion seen on follow-up CTP to reperfusion predicted by post-IAT digital subtraction angiography (DSA) and the ability of the two modalities to predict good 90-day functional outcome in a retrospective study. We found that patients with โ€˜complete reperfusionโ€™ grades on DSA often had ischemic tissue on follow-up CTP and that follow-up CTP had superior specificity and accuracy for predicting functional outcome compared to DSA. In summary, this research has shown that CBF thresholds can reliably detect infarct in AIS and are independent of scan duration, allowing radiation dose to be minimized by limiting scans to 40s without compromising accuracy of infarct volume estimates. Finally, CTP is a more specific and accurate predictor of functional outcome than the commonly used post-procedural DSA, this could help select patients for neuroprotective therapy
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